Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Avaliações

SB

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera

SS

May 17, 2019

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This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

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Neural Networks: Representation

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.

Ministrado por

Andrew Ng

Transcrição

Neural Networks are a pretty old algorithm that was originally motivated by the goal of having machines that can mimic the brain. Now in this class, of course I'm teaching Neural Networks to you because they work really well for different machine learning problems and not, certainly not because they're logically motivated. In this video, I'd like to give you some of the background on Neural Networks. So that we can get a sense of what we can expect them to do. Both in the sense of applying them to modern day machinery problems, as well as for those of you that might be interested in maybe the big AI dream of someday building truly intelligent machines. Also, how Neural Networks might pertain to that. The origins of Neural Networks was as algorithms that try to mimic the brain and those a sense that if we want to build learning systems while why not mimic perhaps the most amazing learning machine we know about, which is perhaps the brain. Neural Networks came to be very widely used throughout the 1980's and 1990's and for various reasons as popularity diminished in the late 90's. But more recently, Neural Networks have had a major recent resurgence. One of the reasons for this resurgence is that Neural Networks are computationally some what more expensive algorithm and so, it was only, you know, maybe somewhat more recently that computers became fast enough to really run large scale Neural Networks and because of that as well as a few other technical reasons which we'll talk about later, modern Neural Networks today are the state of the art technique for many applications. So, when you think about mimicking the brain while one of the human brain does tell me same things, right? The brain can learn to see process images than to hear, learn to process our sense of touch. We can, you know, learn to do math, learn to do calculus, and the brain does so many different and amazing things. It seems like if you want to mimic the brain it seems like you have to write lots of different pieces of software to mimic all of these different fascinating, amazing things that the brain tell us, but does is this fascinating hypothesis that the way the brain does all of these different things is not worth like a thousand different programs, but instead, the way the brain does it is worth just a single learning algorithm. This is just a hypothesis but let me share with you some of the evidence for this. This part of the brain, that little red part of the brain, is your auditory cortex and the way you're understanding my voice now is your ear is taking the sound signal and routing the sound signal to your auditory cortex and that's what's allowing you to understand my words. Neuroscientists have done the following fascinating experiments where you cut the wire from the ears to the auditory cortex and you re-wire, in this case an animal's brain, so that the signal from the eyes to the optic nerve eventually gets routed to the auditory cortex. If you do this it turns out, the auditory cortex will learn to see. And this is in every single sense of the word see as we know it. So, if you do this to the animals, the animals can perform visual discrimination task and as they can look at images and make appropriate decisions based on the images and they're doing it with that piece of brain tissue. Here's another example. That red piece of brain tissue is your somatosensory cortex. That's how you process your sense of touch. If you do a similar re-wiring process then the somatosensory cortex will learn to see. Because of this and other similar experiments, these are called neuro-rewiring experiments. There's this sense that if the same piece of physical brain tissue can process sight or sound or touch then maybe there is one learning algorithm that can process sight or sound or touch. And instead of needing to implement a thousand different programs or a thousand different algorithms to do, you know, the thousand wonderful things that the brain does, maybe what we need to do is figure out some approximation or to whatever the brain's learning algorithm is and implement that and that the brain learned by itself how to process these different types of data. To a surprisingly large extent, it seems as if we can plug in almost any sensor to almost any part of the brain and so, within the reason, the brain will learn to deal with it. Here are a few more examples. On the upper left is an example of learning to see with your tongue. The way it works is--this is actually a system called BrainPort undergoing FDA trials now to help blind people see--but the way it works is, you strap a grayscale camera to your forehead, facing forward, that takes the low resolution grayscale image of what's in front of you and you then run a wire to an array of electrodes that you place on your tongue so that each pixel gets mapped to a location on your tongue where maybe a high voltage corresponds to a dark pixel and a low voltage corresponds to a bright pixel and, even as it does today, with this sort of system you and I will be able to learn to see, you know, in tens of minutes with our tongues. Here's a second example of human echo location or human sonar. So there are two ways you can do this. You can either snap your fingers, or click your tongue. I can't do that very well. But there are blind people today that are actually being trained in schools to do this and learn to interpret the pattern of sounds bouncing off your environment - that's sonar. So, if after you search on YouTube, there are actually videos of this amazing kid who tragically because of cancer had his eyeballs removed, so this is a kid with no eyeballs. But by snapping his fingers, he can walk around and never hit anything. He can ride a skateboard. He can shoot a basketball into a hoop and this is a kid with no eyeballs. Third example is the Haptic Belt where if you have a strap around your waist, ring up buzzers and always have the northmost one buzzing. You can give a human a direction sense similar to maybe how birds can, you know, sense where north is. And, some of the bizarre example, but if you plug a third eye into a frog, the frog will learn to use that eye as well. So, it's pretty amazing to what extent is as if you can plug in almost any sensor to the brain and the brain's learning algorithm will just figure out how to learn from that data and deal with that data. And there's a sense that if we can figure out what the brain's learning algorithm is, and, you know, implement it or implement some approximation to that algorithm on a computer, maybe that would be our best shot at, you know, making real progress towards the AI, the artificial intelligence dream of someday building truly intelligent machines. Now, of course, I'm not teaching Neural Networks, you know, just because they might give us a window into this far-off AI dream, even though I'm personally, that's one of the things that I personally work on in my research life. But the main reason I'm teaching Neural Networks in this class is because it's actually a very effective state of the art technique for modern day machine learning applications. So, in the next few videos, we'll start diving into the technical details of Neural Networks so that you can apply them to modern-day machine learning applications and get them to work well on problems. But for me, you know, one of the reasons the excite me is that maybe they give us this window into what we might do if we're also thinking of what algorithms might someday be able to learn in a manner similar to humankind.